Overview

Dataset statistics

Number of variables46
Number of observations73679
Missing cells198506
Missing cells (%)5.9%
Total size in memory25.9 MiB
Average record size in memory368.0 B

Variable types

Text25
Numeric20
Unsupported1

Alerts

CV_SERVICION1 has constant value ""Constant
TIPO has constant value ""Constant
CV_SERVICION2 has constant value ""Constant
NIVEL has constant value ""Constant
CV_SERVICION3 has constant value ""Constant
SUBNIVEL has constant value ""Constant
CV_CARRERA has constant value ""Constant
C_NOM_CARRERA has 73679 (100.0%) missing valuesMissing
C_MOTIVO has 68772 (93.3%) missing valuesMissing
SUBSISTEMA_1 has 18523 (25.1%) missing valuesMissing
SUBSISTEMA_2 has 18523 (25.1%) missing valuesMissing
SUBSISTEMA_3 has 18523 (25.1%) missing valuesMissing
C_NOM_CARRERA is an unsupported type, check if it needs cleaning or further analysisUnsupported
N_EXTNUM has 45894 (62.3%) zerosZeros
CV_CARACTERIZAN1 has 43367 (58.9%) zerosZeros
CV_CARACTERIZAN2 has 43367 (58.9%) zerosZeros
CV_PLAN_ESTUDIO has 68647 (93.2%) zerosZeros
CV_MOTIVO has 68760 (93.3%) zerosZeros

Reproduction

Analysis started2024-03-10 02:02:21.539187
Analysis finished2024-03-10 02:02:26.237265
Duration4.7 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Distinct16502
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:26.472478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length254
Median length10
Mean length10.03973995
Min length10

Characters and Unicode

Total characters739718
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)0.3%

Sample

1st row01MMS0003C
2nd row01MMS0004B
3rd row01MMS0004B
4th row01MMS0020T
5th row01MMS0022R
ValueCountFrequency (%)
de 50
 
0.1%
25mms0458l 40
 
0.1%
25mms0518j 38
 
0.1%
25mms0457m 34
 
< 0.1%
28mms0445e 32
 
< 0.1%
25mms0452r 28
 
< 0.1%
02mms0227j 28
 
< 0.1%
25mms0475b 27
 
< 0.1%
25mms0456n 27
 
< 0.1%
02mms0191l 26
 
< 0.1%
Other values (16587) 73571
99.6%
2024-03-09T21:02:27.078590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 150222
20.3%
0 113019
15.3%
1 80226
10.8%
S 76527
10.3%
2 54258
 
7.3%
3 37954
 
5.1%
5 32651
 
4.4%
4 29890
 
4.0%
6 25273
 
3.4%
7 24252
 
3.3%
Other values (34) 115446
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 442327
59.8%
Uppercase Letter 296934
40.1%
Math Symbol 233
 
< 0.1%
Space Separator 222
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 150222
50.6%
S 76527
25.8%
Z 5552
 
1.9%
E 3078
 
1.0%
A 3039
 
1.0%
L 3007
 
1.0%
J 2906
 
1.0%
I 2905
 
1.0%
C 2858
 
1.0%
F 2841
 
1.0%
Other values (20) 43999
 
14.8%
Decimal Number
ValueCountFrequency (%)
0 113019
25.6%
1 80226
18.1%
2 54258
12.3%
3 37954
 
8.6%
5 32651
 
7.4%
4 29890
 
6.8%
6 25273
 
5.7%
7 24252
 
5.5%
8 23422
 
5.3%
9 21382
 
4.8%
Math Symbol
ValueCountFrequency (%)
| 233
100.0%
Space Separator
ValueCountFrequency (%)
222
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 442784
59.9%
Latin 296934
40.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 150222
50.6%
S 76527
25.8%
Z 5552
 
1.9%
E 3078
 
1.0%
A 3039
 
1.0%
L 3007
 
1.0%
J 2906
 
1.0%
I 2905
 
1.0%
C 2858
 
1.0%
F 2841
 
1.0%
Other values (20) 43999
 
14.8%
Common
ValueCountFrequency (%)
0 113019
25.5%
1 80226
18.1%
2 54258
12.3%
3 37954
 
8.6%
5 32651
 
7.4%
4 29890
 
6.8%
6 25273
 
5.7%
7 24252
 
5.5%
8 23422
 
5.3%
9 21382
 
4.8%
Other values (4) 457
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 739691
> 99.9%
None 27
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 150222
20.3%
0 113019
15.3%
1 80226
10.8%
S 76527
10.3%
2 54258
 
7.3%
3 37954
 
5.1%
5 32651
 
4.4%
4 29890
 
4.0%
6 25273
 
3.4%
7 24252
 
3.3%
Other values (30) 115419
15.6%
None
ValueCountFrequency (%)
Ú 10
37.0%
Ó 8
29.6%
É 5
18.5%
Á 4
 
14.8%
Distinct17391
Distinct (%)23.6%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:27.549484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length105
Median length88
Mean length35.79321813
Min length4

Characters and Unicode

Total characters2636779
Distinct characters70
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3106 ?
Unique (%)4.2%

Sample

1st rowCENTRO DE ESTUDIOS DE BACHILLERATO 5/1 LIC. JESUS REYES HEROLES
2nd rowCENTRO DE ESTUDIOS DE BACHILLERATO 6/1 AGUASCALIENTES
3rd rowCENTRO DE ESTUDIOS DE BACHILLERATO 6/1 AGUASCALIENTES
4th rowCENTRO DE ATENCION PARA PERSONAS CON DISCAPACIDAD AGUASCALIENTES CBTIS 168
5th rowCENTRO DE ATENCION PARA PERSONAS CON DISCAPACIDAD AGUASCALIENTES CBTIS 282
ValueCountFrequency (%)
de 27693
 
8.1%
telebachillerato 18858
 
5.5%
comunitario 13172
 
3.8%
num 10734
 
3.1%
preparatoria 10093
 
2.9%
colegio 9187
 
2.7%
centro 7874
 
2.3%
plantel 6029
 
1.8%
instituto 5886
 
1.7%
escuela 5533
 
1.6%
Other values (8562) 228149
66.5%
2024-03-09T21:02:28.294589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 290591
11.0%
270055
10.2%
E 262616
10.0%
O 197769
 
7.5%
I 197657
 
7.5%
L 180476
 
6.8%
R 157187
 
6.0%
T 156830
 
5.9%
C 145517
 
5.5%
N 127299
 
4.8%
Other values (60) 650782
24.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2277583
86.4%
Space Separator 270055
 
10.2%
Decimal Number 54253
 
2.1%
Other Punctuation 31460
 
1.2%
Open Punctuation 1261
 
< 0.1%
Close Punctuation 1251
 
< 0.1%
Dash Punctuation 878
 
< 0.1%
Private Use 9
 
< 0.1%
Control 9
 
< 0.1%
Lowercase Letter 8
 
< 0.1%
Other values (3) 12
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 290591
12.8%
E 262616
11.5%
O 197769
8.7%
I 197657
8.7%
L 180476
 
7.9%
R 157187
 
6.9%
T 156830
 
6.9%
C 145517
 
6.4%
N 127299
 
5.6%
U 88493
 
3.9%
Other values (24) 473148
20.8%
Other Punctuation
ValueCountFrequency (%)
. 22834
72.6%
, 4436
 
14.1%
" 3755
 
11.9%
/ 224
 
0.7%
? 104
 
0.3%
' 75
 
0.2%
¿ 21
 
0.1%
@ 4
 
< 0.1%
; 3
 
< 0.1%
2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 10250
18.9%
2 8018
14.8%
0 6256
11.5%
3 5650
10.4%
4 4758
8.8%
8 4332
8.0%
6 4263
7.9%
5 4068
 
7.5%
7 3398
 
6.3%
9 3260
 
6.0%
Control
ValueCountFrequency (%)
“ 6
66.7%
‰ 2
 
22.2%
‘ 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 1259
99.8%
[ 2
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
ż 7
87.5%
ş 1
 
12.5%
Initial Punctuation
ValueCountFrequency (%)
5
83.3%
1
 
16.7%
Space Separator
ValueCountFrequency (%)
270055
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1251
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 878
100.0%
Private Use
ValueCountFrequency (%)
9
100.0%
Currency Symbol
ValueCountFrequency (%)
¤ 3
100.0%
Other Letter
ValueCountFrequency (%)
º 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2277594
86.4%
Common 359176
 
13.6%
Unknown 9
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 290591
12.8%
E 262616
11.5%
O 197769
8.7%
I 197657
8.7%
L 180476
 
7.9%
R 157187
 
6.9%
T 156830
 
6.9%
C 145517
 
6.4%
N 127299
 
5.6%
U 88493
 
3.9%
Other values (27) 473159
20.8%
Common
ValueCountFrequency (%)
270055
75.2%
. 22834
 
6.4%
1 10250
 
2.9%
2 8018
 
2.2%
0 6256
 
1.7%
3 5650
 
1.6%
4 4758
 
1.3%
, 4436
 
1.2%
8 4332
 
1.2%
6 4263
 
1.2%
Other values (22) 18324
 
5.1%
Unknown
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2629631
99.7%
None 7131
 
0.3%
PUA 9
 
< 0.1%
Punctuation 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 290591
11.1%
270055
10.3%
E 262616
10.0%
O 197769
 
7.5%
I 197657
 
7.5%
L 180476
 
6.9%
R 157187
 
6.0%
T 156830
 
6.0%
C 145517
 
5.5%
N 127299
 
4.8%
Other values (40) 643634
24.5%
None
ValueCountFrequency (%)
Ú 2709
38.0%
Ó 1276
17.9%
Ñ 1076
 
15.1%
É 895
 
12.6%
Á 692
 
9.7%
Í 373
 
5.2%
Ü 53
 
0.7%
¿ 21
 
0.3%
à 13
 
0.2%
ż 7
 
0.1%
Other values (6) 16
 
0.2%
PUA
ValueCountFrequency (%)
9
100.0%
Punctuation
ValueCountFrequency (%)
5
62.5%
2
 
25.0%
1
 
12.5%

CV_CCT
Text

Distinct17316
Distinct (%)23.5%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:28.672244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters736670
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique303 ?
Unique (%)0.4%

Sample

1st row01DBP0001U
2nd row01DBP0002T
3rd row01DBP0002T
4th row01DER0004M
5th row01DER0006K
ValueCountFrequency (%)
15pbh3443i 24
 
< 0.1%
28pbh0405p 20
 
< 0.1%
02pbh0130j 18
 
< 0.1%
02pbh0132h 18
 
< 0.1%
25ubh0008e 16
 
< 0.1%
28pbh0013b 16
 
< 0.1%
28pbh0248p 13
 
< 0.1%
28pbh0226d 13
 
< 0.1%
28pbh0214z 13
 
< 0.1%
17ubh0018c 12
 
< 0.1%
Other values (17306) 73504
99.8%
2024-03-09T21:02:29.230943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 148932
20.2%
1 75258
 
10.2%
2 52683
 
7.2%
B 48497
 
6.6%
H 46593
 
6.3%
E 44481
 
6.0%
3 37639
 
5.1%
P 28762
 
3.9%
5 26637
 
3.6%
4 24178
 
3.3%
Other values (26) 203010
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 442002
60.0%
Uppercase Letter 294668
40.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 48497
16.5%
H 46593
15.8%
E 44481
15.1%
P 28762
9.8%
T 24134
8.2%
K 16126
 
5.5%
C 11290
 
3.8%
S 9205
 
3.1%
M 7803
 
2.6%
U 6428
 
2.2%
Other values (16) 51349
17.4%
Decimal Number
ValueCountFrequency (%)
0 148932
33.7%
1 75258
17.0%
2 52683
 
11.9%
3 37639
 
8.5%
5 26637
 
6.0%
4 24178
 
5.5%
6 20285
 
4.6%
7 19735
 
4.5%
8 19117
 
4.3%
9 17538
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 442002
60.0%
Latin 294668
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 48497
16.5%
H 46593
15.8%
E 44481
15.1%
P 28762
9.8%
T 24134
8.2%
K 16126
 
5.5%
C 11290
 
3.8%
S 9205
 
3.1%
M 7803
 
2.6%
U 6428
 
2.2%
Other values (16) 51349
17.4%
Common
ValueCountFrequency (%)
0 148932
33.7%
1 75258
17.0%
2 52683
 
11.9%
3 37639
 
8.5%
5 26637
 
6.0%
4 24178
 
5.5%
6 20285
 
4.6%
7 19735
 
4.5%
8 19117
 
4.3%
9 17538
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 736670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 148932
20.2%
1 75258
 
10.2%
2 52683
 
7.2%
B 48497
 
6.6%
H 46593
 
6.3%
E 44481
 
6.0%
3 37639
 
5.1%
P 28762
 
3.9%
5 26637
 
3.6%
4 24178
 
3.3%
Other values (26) 203010
27.6%

CV_TURNO
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.570635427
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:29.466337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8840188944
Coefficient of variation (CV)0.5628415603
Kurtosis2.082430473
Mean1.570635427
Median Absolute Deviation (MAD)0
Skewness1.690472678
Sum115704
Variance0.7814894056
MonotonicityNot monotonic
2024-03-09T21:02:29.675383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
1 45135
61.3%
2 21258
28.9%
4 6183
 
8.4%
3 1067
 
1.4%
5 24
 
< 0.1%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
1 45135
61.3%
2 21258
28.9%
3 1067
 
1.4%
4 6183
 
8.4%
5 24
 
< 0.1%
ValueCountFrequency (%)
5 24
 
< 0.1%
4 6183
 
8.4%
3 1067
 
1.4%
2 21258
28.9%
1 45135
61.3%
Distinct5
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:29.865439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.828932901
Min length8

Characters and Unicode

Total characters650401
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMATUTINO
2nd rowVESPERTINO
3rd rowVESPERTINO
4th rowDISCONTINUO
5th rowDISCONTINUO
ValueCountFrequency (%)
matutino 45135
61.3%
vespertino 21258
28.9%
discontinuo 6183
 
8.4%
nocturno 1067
 
1.4%
continuo 24
 
< 0.1%
2024-03-09T21:02:30.272975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 118802
18.3%
N 80941
12.4%
O 80941
12.4%
I 78783
12.1%
U 52409
8.1%
M 45135
 
6.9%
A 45135
 
6.9%
E 42516
 
6.5%
S 27441
 
4.2%
R 22325
 
3.4%
Other values (4) 55973
8.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 650401
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 118802
18.3%
N 80941
12.4%
O 80941
12.4%
I 78783
12.1%
U 52409
8.1%
M 45135
 
6.9%
A 45135
 
6.9%
E 42516
 
6.5%
S 27441
 
4.2%
R 22325
 
3.4%
Other values (4) 55973
8.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 650401
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 118802
18.3%
N 80941
12.4%
O 80941
12.4%
I 78783
12.1%
U 52409
8.1%
M 45135
 
6.9%
A 45135
 
6.9%
E 42516
 
6.5%
S 27441
 
4.2%
R 22325
 
3.4%
Other values (4) 55973
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 650401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 118802
18.3%
N 80941
12.4%
O 80941
12.4%
I 78783
12.1%
U 52409
8.1%
M 45135
 
6.9%
A 45135
 
6.9%
E 42516
 
6.5%
S 27441
 
4.2%
R 22325
 
3.4%
Other values (4) 55973
8.6%
Distinct17905
Distinct (%)24.3%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:30.672525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length115
Median length91
Mean length35.98281456
Min length4

Characters and Unicode

Total characters2650746
Distinct characters74
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3318 ?
Unique (%)4.5%

Sample

1st rowCENTRO DE ESTUDIOS DE BACHILLERATO 5/1 LIC. JESUS REYES HEROLES
2nd rowCENTRO DE ESTUDIOS DE BACHILLERATO 6/1 AGUASCALIENTES
3rd rowCENTRO DE ESTUDIOS DE BACHILLERATO 6/1 AGUASCALIENTES
4th rowCENTRO DE ATENCION PARA PERSONAS CON DISCAPACIDAD AGUASCALIENTES CBTIS 168
5th rowCENTRO DE ATENCION PARA PERSONAS CON DISCAPACIDAD AGUASCALIENTES CBTIS 282
ValueCountFrequency (%)
de 27754
 
8.0%
telebachillerato 18854
 
5.5%
comunitario 13156
 
3.8%
num 10567
 
3.1%
preparatoria 10222
 
3.0%
colegio 9213
 
2.7%
centro 7789
 
2.3%
plantel 5926
 
1.7%
instituto 5920
 
1.7%
escuela 5627
 
1.6%
Other values (8668) 230142
66.7%
2024-03-09T21:02:31.556900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 292330
11.0%
271965
10.3%
E 264182
10.0%
O 198720
 
7.5%
I 198600
 
7.5%
L 181079
 
6.8%
R 158237
 
6.0%
T 157568
 
5.9%
C 146010
 
5.5%
N 127864
 
4.8%
Other values (64) 654191
24.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2290305
86.4%
Space Separator 271965
 
10.3%
Decimal Number 54335
 
2.0%
Other Punctuation 30664
 
1.2%
Open Punctuation 1278
 
< 0.1%
Close Punctuation 1264
 
< 0.1%
Dash Punctuation 871
 
< 0.1%
Currency Symbol 22
 
< 0.1%
Private Use 19
 
< 0.1%
Control 8
 
< 0.1%
Other values (4) 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 292330
12.8%
E 264182
11.5%
O 198720
8.7%
I 198600
8.7%
L 181079
 
7.9%
R 158237
 
6.9%
T 157568
 
6.9%
C 146010
 
6.4%
N 127864
 
5.6%
U 88745
 
3.9%
Other values (25) 476970
20.8%
Decimal Number
ValueCountFrequency (%)
1 10253
18.9%
2 8013
14.7%
0 6289
11.6%
3 5651
10.4%
4 4768
8.8%
8 4347
8.0%
6 4257
7.8%
5 4081
 
7.5%
7 3405
 
6.3%
9 3271
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 22738
74.2%
, 4572
 
14.9%
" 2976
 
9.7%
/ 225
 
0.7%
? 84
 
0.3%
' 51
 
0.2%
¿ 12
 
< 0.1%
@ 4
 
< 0.1%
2
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
ż 4
57.1%
s 1
 
14.3%
ş 1
 
14.3%
š 1
 
14.3%
Private Use
ValueCountFrequency (%)
7
36.8%
7
36.8%
5
26.3%
Control
ValueCountFrequency (%)
“ 5
62.5%
‰ 2
 
25.0%
š 1
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 1276
99.8%
[ 2
 
0.2%
Currency Symbol
ValueCountFrequency (%)
¥ 20
90.9%
¤ 2
 
9.1%
Space Separator
ValueCountFrequency (%)
271965
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1264
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 871
100.0%
Initial Punctuation
ValueCountFrequency (%)
4
100.0%
Other Letter
ValueCountFrequency (%)
º 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
¨ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2290315
86.4%
Common 360412
 
13.6%
Unknown 19
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 292330
12.8%
E 264182
11.5%
O 198720
8.7%
I 198600
8.7%
L 181079
 
7.9%
R 158237
 
6.9%
T 157568
 
6.9%
C 146010
 
6.4%
N 127864
 
5.6%
U 88745
 
3.9%
Other values (30) 476980
20.8%
Common
ValueCountFrequency (%)
271965
75.5%
. 22738
 
6.3%
1 10253
 
2.8%
2 8013
 
2.2%
0 6289
 
1.7%
3 5651
 
1.6%
4 4768
 
1.3%
, 4572
 
1.3%
8 4347
 
1.2%
6 4257
 
1.2%
Other values (21) 17559
 
4.9%
Unknown
ValueCountFrequency (%)
7
36.8%
7
36.8%
5
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2642930
99.7%
None 7791
 
0.3%
PUA 19
 
< 0.1%
Punctuation 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 292330
11.1%
271965
10.3%
E 264182
10.0%
O 198720
 
7.5%
I 198600
 
7.5%
L 181079
 
6.9%
R 158237
 
6.0%
T 157568
 
6.0%
C 146010
 
5.5%
N 127864
 
4.8%
Other values (39) 646375
24.5%
None
ValueCountFrequency (%)
Ú 2781
35.7%
Ó 1395
17.9%
Ñ 1212
15.6%
É 1051
 
13.5%
Á 764
 
9.8%
Í 465
 
6.0%
Ü 55
 
0.7%
¥ 20
 
0.3%
¿ 12
 
0.2%
à 11
 
0.1%
Other values (10) 25
 
0.3%
PUA
ValueCountFrequency (%)
7
36.8%
7
36.8%
5
26.3%
Punctuation
ValueCountFrequency (%)
4
66.7%
2
33.3%

CV_ENT_ADMNISTRATIVA
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean17.26864132
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:31.875171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q111
median16
Q324
95-th percentile30
Maximum32
Range31
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.25815685
Coefficient of variation (CV)0.4782169424
Kurtosis-0.9476427786
Mean17.26864132
Median Absolute Deviation (MAD)5
Skewness0.07632099899
Sum1272129
Variance68.19715457
MonotonicityNot monotonic
2024-03-09T21:02:32.255845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
30 7033
 
9.5%
15 6839
 
9.3%
21 6755
 
9.2%
14 4299
 
5.8%
11 4267
 
5.8%
7 3767
 
5.1%
12 3182
 
4.3%
16 3080
 
4.2%
20 2855
 
3.9%
5 2323
 
3.2%
Other values (22) 29267
39.7%
ValueCountFrequency (%)
1 1092
1.5%
2 1419
1.9%
3 520
 
0.7%
4 499
 
0.7%
5 2323
3.2%
ValueCountFrequency (%)
32 1120
 
1.5%
31 1645
 
2.2%
30 7033
9.5%
29 575
 
0.8%
28 2148
 
2.9%
Distinct32
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:32.505426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length31
Median length19
Mean length11.30696241
Min length6

Characters and Unicode

Total characters832950
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGUASCALIENTES
2nd rowAGUASCALIENTES
3rd rowAGUASCALIENTES
4th rowAGUASCALIENTES
5th rowAGUASCALIENTES
ValueCountFrequency (%)
de 21394
 
16.2%
méxico 8764
 
6.6%
llave 7033
 
5.3%
veracruz 7033
 
5.3%
la 7033
 
5.3%
ignacio 7033
 
5.3%
puebla 6755
 
5.1%
jalisco 4299
 
3.2%
guanajuato 4267
 
3.2%
chiapas 3767
 
2.8%
Other values (33) 55041
41.6%
2024-03-09T21:02:33.122453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 135845
16.3%
O 66216
 
7.9%
C 59715
 
7.2%
58752
 
7.1%
E 58356
 
7.0%
I 56383
 
6.8%
L 50147
 
6.0%
U 46184
 
5.5%
R 36138
 
4.3%
N 32242
 
3.9%
Other values (19) 232972
28.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 774198
92.9%
Space Separator 58752
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 135845
17.5%
O 66216
 
8.6%
C 59715
 
7.7%
E 58356
 
7.5%
I 56383
 
7.3%
L 50147
 
6.5%
U 46184
 
6.0%
R 36138
 
4.7%
N 32242
 
4.2%
D 28248
 
3.6%
Other values (18) 204724
26.4%
Space Separator
ValueCountFrequency (%)
58752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 774198
92.9%
Common 58752
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 135845
17.5%
O 66216
 
8.6%
C 59715
 
7.7%
E 58356
 
7.5%
I 56383
 
7.3%
L 50147
 
6.5%
U 46184
 
6.0%
R 36138
 
4.7%
N 32242
 
4.2%
D 28248
 
3.6%
Other values (18) 204724
26.4%
Common
ValueCountFrequency (%)
58752
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 814741
97.8%
None 18209
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 135845
16.7%
O 66216
 
8.1%
C 59715
 
7.3%
58752
 
7.2%
E 58356
 
7.2%
I 56383
 
6.9%
L 50147
 
6.2%
U 46184
 
5.7%
R 36138
 
4.4%
N 32242
 
4.0%
Other values (15) 214763
26.4%
None
ValueCountFrequency (%)
É 9863
54.2%
Á 4725
25.9%
Í 1880
 
10.3%
Ó 1741
 
9.6%

CV_ENT_INMUEBLE
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean17.26864132
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:33.495623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q111
median16
Q324
95-th percentile30
Maximum32
Range31
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.25815685
Coefficient of variation (CV)0.4782169424
Kurtosis-0.9476427786
Mean17.26864132
Median Absolute Deviation (MAD)5
Skewness0.07632099899
Sum1272129
Variance68.19715457
MonotonicityNot monotonic
2024-03-09T21:02:33.841203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
30 7033
 
9.5%
15 6839
 
9.3%
21 6755
 
9.2%
14 4299
 
5.8%
11 4267
 
5.8%
7 3767
 
5.1%
12 3182
 
4.3%
16 3080
 
4.2%
20 2855
 
3.9%
5 2323
 
3.2%
Other values (22) 29267
39.7%
ValueCountFrequency (%)
1 1092
1.5%
2 1419
1.9%
3 520
 
0.7%
4 499
 
0.7%
5 2323
3.2%
ValueCountFrequency (%)
32 1120
 
1.5%
31 1645
 
2.2%
30 7033
9.5%
29 575
 
0.8%
28 2148
 
2.9%
Distinct32
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:34.138892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length31
Median length19
Mean length11.30685382
Min length6

Characters and Unicode

Total characters832942
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGUASCALIENTES
2nd rowAGUASCALIENTES
3rd rowAGUASCALIENTES
4th rowAGUASCALIENTES
5th rowAGUASCALIENTES
ValueCountFrequency (%)
de 21394
 
16.2%
méxico 8764
 
6.6%
llave 7033
 
5.3%
veracruz 7033
 
5.3%
la 7033
 
5.3%
ignacio 7033
 
5.3%
puebla 6755
 
5.1%
jalisco 4299
 
3.2%
guanajuato 4267
 
3.2%
chiapas 3767
 
2.8%
Other values (33) 55039
41.6%
2024-03-09T21:02:34.655737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 135843
16.3%
O 66212
 
7.9%
C 59719
 
7.2%
58750
 
7.1%
E 58360
 
7.0%
I 56381
 
6.8%
L 50147
 
6.0%
U 46182
 
5.5%
R 36136
 
4.3%
N 32238
 
3.9%
Other values (19) 232974
28.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 774192
92.9%
Space Separator 58750
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 135843
17.5%
O 66212
 
8.6%
C 59719
 
7.7%
E 58360
 
7.5%
I 56381
 
7.3%
L 50147
 
6.5%
U 46182
 
6.0%
R 36136
 
4.7%
N 32238
 
4.2%
D 28248
 
3.6%
Other values (18) 204726
26.4%
Space Separator
ValueCountFrequency (%)
58750
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 774192
92.9%
Common 58750
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 135843
17.5%
O 66212
 
8.6%
C 59719
 
7.7%
E 58360
 
7.5%
I 56381
 
7.3%
L 50147
 
6.5%
U 46182
 
6.0%
R 36136
 
4.7%
N 32238
 
4.2%
D 28248
 
3.6%
Other values (18) 204726
26.4%
Common
ValueCountFrequency (%)
58750
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 814733
97.8%
None 18209
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 135843
16.7%
O 66212
 
8.1%
C 59719
 
7.3%
58750
 
7.2%
E 58360
 
7.2%
I 56381
 
6.9%
L 50147
 
6.2%
U 46182
 
5.7%
R 36136
 
4.4%
N 32238
 
4.0%
Other values (15) 214765
26.4%
None
ValueCountFrequency (%)
É 9863
54.2%
Á 4725
25.9%
Í 1880
 
10.3%
Ó 1741
 
9.6%

CV_MUN
Real number (ℝ)

Distinct436
Distinct (%)0.6%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean58.69465297
Minimum1
Maximum570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:35.072473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median37
Q383
95-th percentile183
Maximum570
Range569
Interquartile range (IQR)69

Descriptive statistics

Standard deviation71.49626931
Coefficient of variation (CV)1.218105325
Kurtosis14.61371192
Mean58.69465297
Median Absolute Deviation (MAD)28
Skewness3.179594818
Sum4323859
Variance5111.716525
MonotonicityNot monotonic
2024-03-09T21:02:35.423016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 2128
 
2.9%
1 2034
 
2.8%
4 1968
 
2.7%
2 1769
 
2.4%
5 1617
 
2.2%
7 1616
 
2.2%
3 1606
 
2.2%
17 1599
 
2.2%
6 1570
 
2.1%
114 1494
 
2.0%
Other values (426) 56266
76.4%
ValueCountFrequency (%)
1 2034
2.8%
2 1769
2.4%
3 1606
2.2%
4 1968
2.7%
5 1617
2.2%
ValueCountFrequency (%)
570 4
< 0.1%
569 4
< 0.1%
568 4
< 0.1%
567 4
< 0.1%
566 8
< 0.1%
Distinct2049
Distinct (%)2.8%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:35.806963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length76
Median length38
Mean length11.35703911
Min length3

Characters and Unicode

Total characters836639
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAGUASCALIENTES
2nd rowAGUASCALIENTES
3rd rowAGUASCALIENTES
4th rowAGUASCALIENTES
5th rowAGUASCALIENTES
ValueCountFrequency (%)
de 9610
 
8.0%
san 5213
 
4.3%
juárez 2088
 
1.7%
la 1609
 
1.3%
del 1420
 
1.2%
puebla 1354
 
1.1%
guadalajara 1051
 
0.9%
león 940
 
0.8%
el 849
 
0.7%
villa 828
 
0.7%
Other values (1970) 95910
79.3%
2024-03-09T21:02:36.672188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 129851
15.5%
E 65818
 
7.9%
O 60599
 
7.2%
L 59800
 
7.1%
N 49290
 
5.9%
C 47752
 
5.7%
47205
 
5.6%
T 47086
 
5.6%
I 41398
 
4.9%
R 39467
 
4.7%
Other values (27) 248373
29.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 788686
94.3%
Space Separator 47205
 
5.6%
Other Punctuation 742
 
0.1%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129851
16.5%
E 65818
 
8.3%
O 60599
 
7.7%
L 59800
 
7.6%
N 49290
 
6.2%
C 47752
 
6.1%
T 47086
 
6.0%
I 41398
 
5.2%
R 39467
 
5.0%
U 38587
 
4.9%
Other values (22) 209038
26.5%
Other Punctuation
ValueCountFrequency (%)
. 733
98.8%
, 9
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
3 3
50.0%
Space Separator
ValueCountFrequency (%)
47205
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 788686
94.3%
Common 47953
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129851
16.5%
E 65818
 
8.3%
O 60599
 
7.7%
L 59800
 
7.6%
N 49290
 
6.2%
C 47752
 
6.1%
T 47086
 
6.0%
I 41398
 
5.2%
R 39467
 
5.0%
U 38587
 
4.9%
Other values (22) 209038
26.5%
Common
ValueCountFrequency (%)
47205
98.4%
. 733
 
1.5%
, 9
 
< 0.1%
1 3
 
< 0.1%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 811769
97.0%
None 24870
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129851
16.0%
E 65818
 
8.1%
O 60599
 
7.5%
L 59800
 
7.4%
N 49290
 
6.1%
C 47752
 
5.9%
47205
 
5.8%
T 47086
 
5.8%
I 41398
 
5.1%
R 39467
 
4.9%
Other values (20) 223503
27.5%
None
ValueCountFrequency (%)
Á 12249
49.3%
Ó 4042
 
16.3%
Í 3787
 
15.2%
É 3426
 
13.8%
Ú 735
 
3.0%
Ñ 603
 
2.4%
Ü 28
 
0.1%

CV_LOC
Real number (ℝ)

Distinct640
Distinct (%)0.9%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean41.15571423
Minimum1
Maximum3381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:36.963955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q325
95-th percentile184
Maximum3381
Range3380
Interquartile range (IQR)24

Descriptive statistics

Standard deviation147.7879034
Coefficient of variation (CV)3.590944931
Kurtosis165.9077397
Mean41.15571423
Median Absolute Deviation (MAD)0
Skewness10.64138163
Sum3031818
Variance21841.26439
MonotonicityNot monotonic
2024-03-09T21:02:37.231456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 41902
56.9%
2 993
 
1.3%
3 838
 
1.1%
5 823
 
1.1%
4 814
 
1.1%
8 736
 
1.0%
6 703
 
1.0%
9 635
 
0.9%
11 600
 
0.8%
7 581
 
0.8%
Other values (630) 25042
34.0%
ValueCountFrequency (%)
1 41902
56.9%
2 993
 
1.3%
3 838
 
1.1%
4 814
 
1.1%
5 823
 
1.1%
ValueCountFrequency (%)
3381 4
< 0.1%
3368 4
< 0.1%
3333 4
< 0.1%
3263 4
< 0.1%
3217 4
< 0.1%
Distinct8043
Distinct (%)10.9%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:37.747133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length76
Median length48
Mean length14.41782616
Min length3

Characters and Unicode

Total characters1062118
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)0.1%

Sample

1st rowAGUASCALIENTES
2nd rowAGUASCALIENTES
3rd rowAGUASCALIENTES
4th rowAGUASCALIENTES
5th rowAGUASCALIENTES
ValueCountFrequency (%)
de 14794
 
9.2%
san 8501
 
5.3%
el 3928
 
2.4%
la 3822
 
2.4%
los 2365
 
1.5%
ciudad 2332
 
1.4%
santa 1961
 
1.2%
del 1952
 
1.2%
juárez 1943
 
1.2%
heroica 1908
 
1.2%
Other values (6260) 117967
73.1%
2024-03-09T21:02:39.112854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 159780
15.0%
87806
 
8.3%
E 84309
 
7.9%
O 76326
 
7.2%
L 71604
 
6.7%
N 60250
 
5.7%
C 55205
 
5.2%
I 53493
 
5.0%
R 52901
 
5.0%
T 48660
 
4.6%
Other values (43) 311784
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 964672
90.8%
Space Separator 87820
 
8.3%
Open Punctuation 3763
 
0.4%
Close Punctuation 3763
 
0.4%
Other Punctuation 841
 
0.1%
Decimal Number 691
 
0.1%
Dash Punctuation 568
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 159780
16.6%
E 84309
 
8.7%
O 76326
 
7.9%
L 71604
 
7.4%
N 60250
 
6.2%
C 55205
 
5.7%
I 53493
 
5.5%
R 52901
 
5.5%
T 48660
 
5.0%
S 44182
 
4.6%
Other values (23) 257962
26.7%
Decimal Number
ValueCountFrequency (%)
1 234
33.9%
2 188
27.2%
3 82
 
11.9%
0 72
 
10.4%
4 32
 
4.6%
6 28
 
4.1%
8 23
 
3.3%
5 16
 
2.3%
7 8
 
1.2%
9 8
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 823
97.9%
, 9
 
1.1%
' 9
 
1.1%
Space Separator
ValueCountFrequency (%)
87806
> 99.9%
  14
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 3653
97.1%
[ 110
 
2.9%
Close Punctuation
ValueCountFrequency (%)
) 3653
97.1%
] 110
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 964672
90.8%
Common 97446
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 159780
16.6%
E 84309
 
8.7%
O 76326
 
7.9%
L 71604
 
7.4%
N 60250
 
6.2%
C 55205
 
5.7%
I 53493
 
5.5%
R 52901
 
5.5%
T 48660
 
5.0%
S 44182
 
4.6%
Other values (23) 257962
26.7%
Common
ValueCountFrequency (%)
87806
90.1%
( 3653
 
3.7%
) 3653
 
3.7%
. 823
 
0.8%
- 568
 
0.6%
1 234
 
0.2%
2 188
 
0.2%
] 110
 
0.1%
[ 110
 
0.1%
3 82
 
0.1%
Other values (10) 219
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1034969
97.4%
None 27149
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 159780
15.4%
87806
 
8.5%
E 84309
 
8.1%
O 76326
 
7.4%
L 71604
 
6.9%
N 60250
 
5.8%
C 55205
 
5.3%
I 53493
 
5.2%
R 52901
 
5.1%
T 48660
 
4.7%
Other values (35) 284635
27.5%
None
ValueCountFrequency (%)
Á 10251
37.8%
Ó 5936
21.9%
Í 5193
19.1%
É 3797
 
14.0%
Ú 969
 
3.6%
Ñ 949
 
3.5%
Ü 40
 
0.1%
  14
 
0.1%
Distinct10016
Distinct (%)13.6%
Missing18
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:39.474433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length183
Median length132
Mean length22.2778268
Min length1

Characters and Unicode

Total characters1641007
Distinct characters97
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1153 ?
Unique (%)1.6%

Sample

1st rowBOULEVARD NAZARIO ORTIZ GARZA
2nd rowCALLE SAN FRANCISCO DE LOS VIVEROS
3rd rowCALLE SAN FRANCISCO DE LOS VIVEROS
4th rowCALLE RIO RHIN
5th rowAVENIDA VIÑEDOS RIVIER
ValueCountFrequency (%)
calle 46060
 
18.8%
ninguno 20081
 
8.2%
avenida 10103
 
4.1%
de 8903
 
3.6%
carretera 4664
 
1.9%
conocido 3492
 
1.4%
la 2730
 
1.1%
kilometro 2461
 
1.0%
a 2163
 
0.9%
san 1934
 
0.8%
Other values (6105) 142020
58.1%
2024-03-09T21:02:40.166871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 201628
12.3%
175111
10.7%
E 160326
9.8%
L 154306
9.4%
N 134617
 
8.2%
O 120567
 
7.3%
I 103709
 
6.3%
C 102989
 
6.3%
R 87020
 
5.3%
D 51456
 
3.1%
Other values (87) 349278
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1433733
87.4%
Space Separator 175113
 
10.7%
Decimal Number 22775
 
1.4%
Dash Punctuation 3315
 
0.2%
Other Punctuation 3296
 
0.2%
Math Symbol 963
 
0.1%
Lowercase Letter 677
 
< 0.1%
Open Punctuation 569
 
< 0.1%
Close Punctuation 545
 
< 0.1%
Currency Symbol 6
 
< 0.1%
Other values (4) 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 201628
14.1%
E 160326
11.2%
L 154306
10.8%
N 134617
9.4%
O 120567
8.4%
I 103709
 
7.2%
C 102989
 
7.2%
R 87020
 
6.1%
D 51456
 
3.6%
U 50142
 
3.5%
Other values (26) 266973
18.6%
Lowercase Letter
ValueCountFrequency (%)
a 95
14.0%
e 90
13.3%
r 70
10.3%
o 60
8.9%
i 46
 
6.8%
t 42
 
6.2%
l 38
 
5.6%
n 36
 
5.3%
s 33
 
4.9%
d 32
 
4.7%
Other values (15) 135
19.9%
Decimal Number
ValueCountFrequency (%)
1 5006
22.0%
0 3792
16.6%
5 3246
14.3%
2 3104
13.6%
3 1872
 
8.2%
6 1542
 
6.8%
4 1387
 
6.1%
7 1003
 
4.4%
8 945
 
4.1%
9 878
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 2977
90.3%
, 146
 
4.4%
/ 91
 
2.8%
? 65
 
2.0%
; 8
 
0.2%
" 6
 
0.2%
' 3
 
0.1%
Private Use
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%
Space Separator
ValueCountFrequency (%)
175111
> 99.9%
  2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3314
> 99.9%
1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 962
99.9%
| 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 565
99.3%
[ 4
 
0.7%
Close Punctuation
ValueCountFrequency (%)
) 541
99.3%
} 4
 
0.7%
Other Symbol
ValueCountFrequency (%)
° 4
66.7%
¦ 2
33.3%
Control
ValueCountFrequency (%)
š 2
66.7%
– 1
33.3%
Currency Symbol
ValueCountFrequency (%)
¥ 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
¨ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1434410
87.4%
Common 206592
 
12.6%
Unknown 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 201628
14.1%
E 160326
11.2%
L 154306
10.8%
N 134617
9.4%
O 120567
8.4%
I 103709
 
7.2%
C 102989
 
7.2%
R 87020
 
6.1%
D 51456
 
3.6%
U 50142
 
3.5%
Other values (51) 267650
18.7%
Common
ValueCountFrequency (%)
175111
84.8%
1 5006
 
2.4%
0 3792
 
1.8%
- 3314
 
1.6%
5 3246
 
1.6%
2 3104
 
1.5%
. 2977
 
1.4%
3 1872
 
0.9%
6 1542
 
0.7%
4 1387
 
0.7%
Other values (23) 5241
 
2.5%
Unknown
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1633470
99.5%
None 7531
 
0.5%
PUA 5
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 201628
12.3%
175111
10.7%
E 160326
9.8%
L 154306
9.4%
N 134617
 
8.2%
O 120567
 
7.4%
I 103709
 
6.3%
C 102989
 
6.3%
R 87020
 
5.3%
D 51456
 
3.2%
Other values (63) 341741
20.9%
None
ValueCountFrequency (%)
Ó 3087
41.0%
Á 1299
17.2%
Í 1093
 
14.5%
É 1024
 
13.6%
Ñ 882
 
11.7%
Ú 103
 
1.4%
ó 10
 
0.1%
¥ 6
 
0.1%
à 4
 
0.1%
° 4
 
0.1%
Other values (10) 19
 
0.3%
PUA
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

N_EXTNUM
Real number (ℝ)

ZEROS 

Distinct1606
Distinct (%)2.2%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean322.4753825
Minimum0
Maximum34900
Zeros45894
Zeros (%)62.3%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:40.515683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3100
95-th percentile1504
Maximum34900
Range34900
Interquartile range (IQR)100

Descriptive statistics

Standard deviation1426.844749
Coefficient of variation (CV)4.424662552
Kurtosis165.2637751
Mean322.4753825
Median Absolute Deviation (MAD)0
Skewness10.8597021
Sum23755794
Variance2035885.937
MonotonicityNot monotonic
2024-03-09T21:02:40.831122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45894
62.3%
1 928
 
1.3%
2 488
 
0.7%
5 370
 
0.5%
100 344
 
0.5%
3 337
 
0.5%
4 291
 
0.4%
10 249
 
0.3%
12 222
 
0.3%
6 219
 
0.3%
Other values (1596) 24325
33.0%
ValueCountFrequency (%)
0 45894
62.3%
1 928
 
1.3%
2 488
 
0.7%
3 337
 
0.5%
4 291
 
0.4%
ValueCountFrequency (%)
34900 1
 
< 0.1%
33982 4
< 0.1%
33419 4
< 0.1%
33205 4
< 0.1%
31219 8
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:41.038981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters515669
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPÚBLICO
2nd rowPÚBLICO
3rd rowPÚBLICO
4th rowPÚBLICO
5th rowPÚBLICO
ValueCountFrequency (%)
público 46566
63.2%
privado 27101
36.8%
2024-03-09T21:02:41.572418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 73667
14.3%
I 73667
14.3%
O 73667
14.3%
Ú 46566
9.0%
B 46566
9.0%
L 46566
9.0%
C 46566
9.0%
R 27101
 
5.3%
V 27101
 
5.3%
A 27101
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 515669
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 73667
14.3%
I 73667
14.3%
O 73667
14.3%
Ú 46566
9.0%
B 46566
9.0%
L 46566
9.0%
C 46566
9.0%
R 27101
 
5.3%
V 27101
 
5.3%
A 27101
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 515669
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 73667
14.3%
I 73667
14.3%
O 73667
14.3%
Ú 46566
9.0%
B 46566
9.0%
L 46566
9.0%
C 46566
9.0%
R 27101
 
5.3%
V 27101
 
5.3%
A 27101
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 469103
91.0%
None 46566
 
9.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 73667
15.7%
I 73667
15.7%
O 73667
15.7%
B 46566
9.9%
L 46566
9.9%
C 46566
9.9%
R 27101
 
5.8%
V 27101
 
5.8%
A 27101
 
5.8%
D 27101
 
5.8%
None
ValueCountFrequency (%)
Ú 46566
100.0%
Distinct6
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:41.805775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length19
Median length7
Mean length7.073411433
Min length7

Characters and Unicode

Total characters521077
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFEDERAL
2nd rowFEDERAL
3rd rowFEDERAL
4th rowFEDERAL
5th rowFEDERAL
ValueCountFrequency (%)
estatal 40650
55.2%
privado 25678
34.9%
autónomo 3973
 
5.4%
federal 1943
 
2.6%
subsidio 1423
 
1.9%
transferido 1
 
< 0.1%
2024-03-09T21:02:42.233378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 112895
21.7%
T 85274
16.4%
E 44537
 
8.5%
S 43497
 
8.3%
L 42593
 
8.2%
O 35048
 
6.7%
D 29045
 
5.6%
I 28525
 
5.5%
R 27623
 
5.3%
V 25678
 
4.9%
Other values (8) 46362
8.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 521076
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 112895
21.7%
T 85274
16.4%
E 44537
 
8.5%
S 43497
 
8.3%
L 42593
 
8.2%
O 35048
 
6.7%
D 29045
 
5.6%
I 28525
 
5.5%
R 27623
 
5.3%
V 25678
 
4.9%
Other values (7) 46361
8.9%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 521076
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 112895
21.7%
T 85274
16.4%
E 44537
 
8.5%
S 43497
 
8.3%
L 42593
 
8.2%
O 35048
 
6.7%
D 29045
 
5.6%
I 28525
 
5.5%
R 27623
 
5.3%
V 25678
 
4.9%
Other values (7) 46361
8.9%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517104
99.2%
None 3973
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 112895
21.8%
T 85274
16.5%
E 44537
 
8.6%
S 43497
 
8.4%
L 42593
 
8.2%
O 35048
 
6.8%
D 29045
 
5.6%
I 28525
 
5.5%
R 27623
 
5.3%
V 25678
 
5.0%
Other values (7) 42389
 
8.2%
None
ValueCountFrequency (%)
Ó 3973
100.0%

CV_SERVICION1
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3
Minimum3
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:42.453502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median3
Q33
95-th percentile3
Maximum3
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean3
Median Absolute Deviation (MAD)0
Skewness0
Sum221001
Variance0
MonotonicityIncreasing
2024-03-09T21:02:42.638904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
3 73667
> 99.9%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
3 73667
> 99.9%
ValueCountFrequency (%)
3 73667
> 99.9%

TIPO
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:42.805355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1031338
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEDIA SUPERIOR
2nd rowMEDIA SUPERIOR
3rd rowMEDIA SUPERIOR
4th rowMEDIA SUPERIOR
5th rowMEDIA SUPERIOR
ValueCountFrequency (%)
media 73667
50.0%
superior 73667
50.0%
2024-03-09T21:02:43.220655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 147334
14.3%
I 147334
14.3%
R 147334
14.3%
M 73667
7.1%
D 73667
7.1%
A 73667
7.1%
73667
7.1%
S 73667
7.1%
U 73667
7.1%
P 73667
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 957671
92.9%
Space Separator 73667
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 147334
15.4%
I 147334
15.4%
R 147334
15.4%
M 73667
7.7%
D 73667
7.7%
A 73667
7.7%
S 73667
7.7%
U 73667
7.7%
P 73667
7.7%
O 73667
7.7%
Space Separator
ValueCountFrequency (%)
73667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 957671
92.9%
Common 73667
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 147334
15.4%
I 147334
15.4%
R 147334
15.4%
M 73667
7.7%
D 73667
7.7%
A 73667
7.7%
S 73667
7.7%
U 73667
7.7%
P 73667
7.7%
O 73667
7.7%
Common
ValueCountFrequency (%)
73667
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1031338
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 147334
14.3%
I 147334
14.3%
R 147334
14.3%
M 73667
7.1%
D 73667
7.1%
A 73667
7.1%
73667
7.1%
S 73667
7.1%
U 73667
7.1%
P 73667
7.1%

CV_SERVICION2
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:43.422872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum73667
Variance0
MonotonicityIncreasing
2024-03-09T21:02:43.614617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 73667
> 99.9%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
1 73667
> 99.9%
ValueCountFrequency (%)
1 73667
> 99.9%

NIVEL
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:43.774827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1031338
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEDIA SUPERIOR
2nd rowMEDIA SUPERIOR
3rd rowMEDIA SUPERIOR
4th rowMEDIA SUPERIOR
5th rowMEDIA SUPERIOR
ValueCountFrequency (%)
media 73667
50.0%
superior 73667
50.0%
2024-03-09T21:02:44.166725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 147334
14.3%
I 147334
14.3%
R 147334
14.3%
M 73667
7.1%
D 73667
7.1%
A 73667
7.1%
73667
7.1%
S 73667
7.1%
U 73667
7.1%
P 73667
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 957671
92.9%
Space Separator 73667
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 147334
15.4%
I 147334
15.4%
R 147334
15.4%
M 73667
7.7%
D 73667
7.7%
A 73667
7.7%
S 73667
7.7%
U 73667
7.7%
P 73667
7.7%
O 73667
7.7%
Space Separator
ValueCountFrequency (%)
73667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 957671
92.9%
Common 73667
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 147334
15.4%
I 147334
15.4%
R 147334
15.4%
M 73667
7.7%
D 73667
7.7%
A 73667
7.7%
S 73667
7.7%
U 73667
7.7%
P 73667
7.7%
O 73667
7.7%
Common
ValueCountFrequency (%)
73667
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1031338
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 147334
14.3%
I 147334
14.3%
R 147334
14.3%
M 73667
7.1%
D 73667
7.1%
A 73667
7.1%
73667
7.1%
S 73667
7.1%
U 73667
7.1%
P 73667
7.1%

CV_SERVICION3
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:44.370836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum73667
Variance0
MonotonicityIncreasing
2024-03-09T21:02:44.543578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 73667
> 99.9%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
1 73667
> 99.9%
ValueCountFrequency (%)
1 73667
> 99.9%

SUBNIVEL
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:44.705753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters1473340
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBACHILLERATO GENERAL
2nd rowBACHILLERATO GENERAL
3rd rowBACHILLERATO GENERAL
4th rowBACHILLERATO GENERAL
5th rowBACHILLERATO GENERAL
ValueCountFrequency (%)
bachillerato 73667
50.0%
general 73667
50.0%
2024-03-09T21:02:45.089691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 221001
15.0%
L 221001
15.0%
E 221001
15.0%
R 147334
10.0%
B 73667
 
5.0%
C 73667
 
5.0%
H 73667
 
5.0%
I 73667
 
5.0%
T 73667
 
5.0%
O 73667
 
5.0%
Other values (3) 221001
15.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1399673
95.0%
Space Separator 73667
 
5.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 221001
15.8%
L 221001
15.8%
E 221001
15.8%
R 147334
10.5%
B 73667
 
5.3%
C 73667
 
5.3%
H 73667
 
5.3%
I 73667
 
5.3%
T 73667
 
5.3%
O 73667
 
5.3%
Other values (2) 147334
10.5%
Space Separator
ValueCountFrequency (%)
73667
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1399673
95.0%
Common 73667
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 221001
15.8%
L 221001
15.8%
E 221001
15.8%
R 147334
10.5%
B 73667
 
5.3%
C 73667
 
5.3%
H 73667
 
5.3%
I 73667
 
5.3%
T 73667
 
5.3%
O 73667
 
5.3%
Other values (2) 147334
10.5%
Common
ValueCountFrequency (%)
73667
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1473340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 221001
15.0%
L 221001
15.0%
E 221001
15.0%
R 147334
10.0%
B 73667
 
5.0%
C 73667
 
5.0%
H 73667
 
5.0%
I 73667
 
5.0%
T 73667
 
5.0%
O 73667
 
5.0%
Other values (3) 221001
15.0%

CV_CARACTERIZAN1
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.4113103561
Minimum0
Maximum1
Zeros43367
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:45.305645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4920746224
Coefficient of variation (CV)1.196358456
Kurtosis-1.870103413
Mean0.4113103561
Median Absolute Deviation (MAD)0
Skewness0.3604821213
Sum30300
Variance0.242137434
MonotonicityNot monotonic
2024-03-09T21:02:45.485814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 43367
58.9%
1 30300
41.1%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
0 43367
58.9%
1 30300
41.1%
ValueCountFrequency (%)
1 30300
41.1%
0 43367
58.9%
Distinct2
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:45.662579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters663003
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSERVICIOS
2nd rowSERVICIOS
3rd rowSERVICIOS
4th rowSERVICIOS
5th rowSERVICIOS
ValueCountFrequency (%)
no 43367
37.1%
aplica 43367
37.1%
servicios 30300
25.9%
2024-03-09T21:02:46.073189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 103967
15.7%
A 86734
13.1%
O 73667
11.1%
C 73667
11.1%
S 60600
9.1%
N 43367
6.5%
43367
6.5%
P 43367
6.5%
L 43367
6.5%
E 30300
 
4.6%
Other values (2) 60600
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 619636
93.5%
Space Separator 43367
 
6.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 103967
16.8%
A 86734
14.0%
O 73667
11.9%
C 73667
11.9%
S 60600
9.8%
N 43367
7.0%
P 43367
7.0%
L 43367
7.0%
E 30300
 
4.9%
R 30300
 
4.9%
Space Separator
ValueCountFrequency (%)
43367
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 619636
93.5%
Common 43367
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 103967
16.8%
A 86734
14.0%
O 73667
11.9%
C 73667
11.9%
S 60600
9.8%
N 43367
7.0%
P 43367
7.0%
L 43367
7.0%
E 30300
 
4.9%
R 30300
 
4.9%
Common
ValueCountFrequency (%)
43367
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 103967
15.7%
A 86734
13.1%
O 73667
11.1%
C 73667
11.1%
S 60600
9.1%
N 43367
6.5%
43367
6.5%
P 43367
6.5%
L 43367
6.5%
E 30300
 
4.6%
Other values (2) 60600
9.1%

CV_CARACTERIZAN2
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.70399229
Minimum0
Maximum66
Zeros43367
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:46.277195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q334
95-th percentile35
Maximum66
Range66
Interquartile range (IQR)34

Descriptive statistics

Standard deviation16.56217661
Coefficient of variation (CV)1.208565815
Kurtosis-1.806164504
Mean13.70399229
Median Absolute Deviation (MAD)0
Skewness0.401589566
Sum1009532
Variance274.305694
MonotonicityNot monotonic
2024-03-09T21:02:46.496813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 43367
58.9%
35 13326
 
18.1%
34 8104
 
11.0%
31 5002
 
6.8%
30 1282
 
1.7%
36 1143
 
1.6%
28 511
 
0.7%
10 415
 
0.6%
29 221
 
0.3%
32 192
 
0.3%
Other values (6) 104
 
0.1%
ValueCountFrequency (%)
0 43367
58.9%
6 76
 
0.1%
10 415
 
0.6%
28 511
 
0.7%
29 221
 
0.3%
ValueCountFrequency (%)
66 7
 
< 0.1%
59 5
 
< 0.1%
58 4
 
< 0.1%
49 4
 
< 0.1%
36 1143
1.6%
Distinct16
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:46.740709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length33
Median length9
Mean length14.65885675
Min length4

Characters and Unicode

Total characters1079874
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBACHILLERATO PEDAGÓGICO
2nd rowBACHILLERATO PEDAGÓGICO
3rd rowBACHILLERATO PEDAGÓGICO
4th rowCAED
5th rowCAED
ValueCountFrequency (%)
no 43367
30.1%
aplica 43367
30.1%
telebachillerato 21430
14.9%
comunitario 13518
 
9.4%
bachillerato 6710
 
4.7%
a 5002
 
3.5%
distancia 5002
 
3.5%
por 1282
 
0.9%
cooperación 1282
 
0.9%
caed 1143
 
0.8%
Other values (13) 1965
 
1.4%
2024-03-09T21:02:47.185689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 175017
16.2%
L 121812
11.3%
I 111729
10.3%
O 103559
9.6%
C 94985
8.8%
E 76811
7.1%
70401
6.5%
T 69320
 
6.4%
N 64722
 
6.0%
P 46677
 
4.3%
Other values (12) 144841
13.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1009473
93.5%
Space Separator 70401
 
6.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 175017
17.3%
L 121812
12.1%
I 111729
11.1%
O 103559
10.3%
C 94985
9.4%
E 76811
7.6%
T 69320
 
6.9%
N 64722
 
6.4%
P 46677
 
4.6%
R 45569
 
4.5%
Other values (11) 99272
9.8%
Space Separator
ValueCountFrequency (%)
70401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1009473
93.5%
Common 70401
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 175017
17.3%
L 121812
12.1%
I 111729
11.1%
O 103559
10.3%
C 94985
9.4%
E 76811
7.6%
T 69320
 
6.9%
N 64722
 
6.4%
P 46677
 
4.6%
R 45569
 
4.5%
Other values (11) 99272
9.8%
Common
ValueCountFrequency (%)
70401
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1078371
99.9%
None 1503
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 175017
16.2%
L 121812
11.3%
I 111729
10.4%
O 103559
9.6%
C 94985
8.8%
E 76811
7.1%
70401
6.5%
T 69320
 
6.4%
N 64722
 
6.0%
P 46677
 
4.3%
Other values (11) 143338
13.3%
None
ValueCountFrequency (%)
Ó 1503
100.0%

CV_CARRERA
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean99999
Minimum99999
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:47.429249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum99999
5-th percentile99999
Q199999
median99999
Q399999
95-th percentile99999
Maximum99999
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean99999
Median Absolute Deviation (MAD)0
Skewness0
Sum7366626333
Variance0
MonotonicityIncreasing
2024-03-09T21:02:47.622062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
99999 73667
> 99.9%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
99999 73667
> 99.9%
ValueCountFrequency (%)
99999 73667
> 99.9%

C_NOM_CARRERA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing73679
Missing (%)100.0%
Memory size575.7 KiB

CV_MODALIDAD
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.125836535
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:47.830074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum3
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4108969531
Coefficient of variation (CV)0.3649703491
Kurtosis11.11339084
Mean1.125836535
Median Absolute Deviation (MAD)0
Skewness3.410686176
Sum82937
Variance0.1688363061
MonotonicityNot monotonic
2024-03-09T21:02:48.059393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1 66564
90.3%
2 4936
 
6.7%
3 2167
 
2.9%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
1 66564
90.3%
2 4936
 
6.7%
3 2167
 
2.9%
ValueCountFrequency (%)
3 2167
 
2.9%
2 4936
 
6.7%
1 66564
90.3%
Distinct3
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:48.332155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length15
Median length12
Mean length11.61921892
Min length5

Characters and Unicode

Total characters855953
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESCOLARIZADA
2nd rowESCOLARIZADA
3rd rowESCOLARIZADA
4th rowNO ESCOLARIZADA
5th rowNO ESCOLARIZADA
ValueCountFrequency (%)
escolarizada 68731
90.6%
mixta 4936
 
6.5%
no 2167
 
2.9%
2024-03-09T21:02:48.798680image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 211129
24.7%
I 73667
 
8.6%
O 70898
 
8.3%
E 68731
 
8.0%
S 68731
 
8.0%
C 68731
 
8.0%
L 68731
 
8.0%
R 68731
 
8.0%
Z 68731
 
8.0%
D 68731
 
8.0%
Other values (5) 19142
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 853786
99.7%
Space Separator 2167
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 211129
24.7%
I 73667
 
8.6%
O 70898
 
8.3%
E 68731
 
8.1%
S 68731
 
8.1%
C 68731
 
8.1%
L 68731
 
8.1%
R 68731
 
8.1%
Z 68731
 
8.1%
D 68731
 
8.1%
Other values (4) 16975
 
2.0%
Space Separator
ValueCountFrequency (%)
2167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 853786
99.7%
Common 2167
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 211129
24.7%
I 73667
 
8.6%
O 70898
 
8.3%
E 68731
 
8.1%
S 68731
 
8.1%
C 68731
 
8.1%
L 68731
 
8.1%
R 68731
 
8.1%
Z 68731
 
8.1%
D 68731
 
8.1%
Other values (4) 16975
 
2.0%
Common
ValueCountFrequency (%)
2167
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 855953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 211129
24.7%
I 73667
 
8.6%
O 70898
 
8.3%
E 68731
 
8.0%
S 68731
 
8.0%
C 68731
 
8.0%
L 68731
 
8.0%
R 68731
 
8.0%
Z 68731
 
8.0%
D 68731
 
8.0%
Other values (5) 19142
 
2.2%

CV_OPCION_EDUCATIVA
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.292600486
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:49.007304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9421688778
Coefficient of variation (CV)0.7288941077
Kurtosis13.82602875
Mean1.292600486
Median Absolute Deviation (MAD)0
Skewness3.676633826
Sum95222
Variance0.8876821943
MonotonicityNot monotonic
2024-03-09T21:02:49.294153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 65472
88.9%
3 4618
 
6.3%
6 1535
 
2.1%
2 1092
 
1.5%
5 610
 
0.8%
4 318
 
0.4%
8 18
 
< 0.1%
9 4
 
< 0.1%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
1 65472
88.9%
2 1092
 
1.5%
3 4618
 
6.3%
4 318
 
0.4%
5 610
 
0.8%
ValueCountFrequency (%)
9 4
 
< 0.1%
8 18
 
< 0.1%
6 1535
2.1%
5 610
 
0.8%
4 318
 
0.4%
Distinct10
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:49.515569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length40
Median length10
Mean length10.28156434
Min length5

Characters and Unicode

Total characters757412
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESENCIAL
2nd rowPRESENCIAL
3rd rowPRESENCIAL
4th rowCERTIFICACIÓN POR EVALUACIÓNES PARCIALES
5th rowCERTIFICACIÓN POR EVALUACIÓNES PARCIALES
ValueCountFrequency (%)
presencial 65472
83.6%
mixta 4618
 
5.9%
certificación 1535
 
2.0%
por 1535
 
2.0%
parciales 1535
 
2.0%
evaluaciónes 1148
 
1.5%
intensiva 1092
 
1.4%
virtual 614
 
0.8%
evaluaciones 387
 
0.5%
autoplaneada 318
 
0.4%
Other values (6) 48
 
0.1%
2024-03-09T21:02:49.963032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 138538
18.3%
A 80765
10.7%
I 80583
10.6%
C 73165
9.7%
N 71088
9.4%
R 70691
9.3%
S 69634
9.2%
L 69478
9.2%
P 68860
9.1%
T 8177
 
1.1%
Other values (11) 26433
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 752777
99.4%
Space Separator 4635
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 138538
18.4%
A 80765
10.7%
I 80583
10.7%
C 73165
9.7%
N 71088
9.4%
R 70691
9.4%
S 69634
9.3%
L 69478
9.2%
P 68860
9.1%
T 8177
 
1.1%
Other values (10) 21798
 
2.9%
Space Separator
ValueCountFrequency (%)
4635
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 752777
99.4%
Common 4635
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 138538
18.4%
A 80765
10.7%
I 80583
10.7%
C 73165
9.7%
N 71088
9.4%
R 70691
9.4%
S 69634
9.3%
L 69478
9.2%
P 68860
9.1%
T 8177
 
1.1%
Other values (10) 21798
 
2.9%
Common
ValueCountFrequency (%)
4635
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 754709
99.6%
None 2703
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 138538
18.4%
A 80765
10.7%
I 80583
10.7%
C 73165
9.7%
N 71088
9.4%
R 70691
9.4%
S 69634
9.2%
L 69478
9.2%
P 68860
9.1%
T 8177
 
1.1%
Other values (8) 23730
 
3.1%
None
ValueCountFrequency (%)
Ó 2683
99.3%
Ú 18
 
0.7%
Í 2
 
0.1%

CV_PLAN_ESTUDIO
Real number (ℝ)

ZEROS 

Distinct47
Distinct (%)0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean137.1003027
Minimum0
Maximum2017
Zeros68647
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:50.214308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2009
Maximum2017
Range2017
Interquartile range (IQR)0

Descriptive statistics

Standard deviation506.9930253
Coefficient of variation (CV)3.697971597
Kurtosis9.749011424
Mean137.1003027
Median Absolute Deviation (MAD)0
Skewness3.427626776
Sum10099768
Variance257041.9278
MonotonicityNot monotonic
2024-03-09T21:02:50.496952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 68647
93.2%
2014 1113
 
1.5%
2009 750
 
1.0%
2017 749
 
1.0%
2016 445
 
0.6%
2013 399
 
0.5%
2015 358
 
0.5%
2010 273
 
0.4%
2008 155
 
0.2%
2011 134
 
0.2%
Other values (37) 644
 
0.9%
ValueCountFrequency (%)
0 68647
93.2%
1960 1
 
< 0.1%
1961 2
 
< 0.1%
1963 1
 
< 0.1%
1972 2
 
< 0.1%
ValueCountFrequency (%)
2017 749
1.0%
2016 445
 
0.6%
2015 358
 
0.5%
2014 1113
1.5%
2013 399
 
0.5%

DURACION
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean826.9835069
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:50.738779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range997
Interquartile range (IQR)0

Descriptive statistics

Standard deviation376.2321499
Coefficient of variation (CV)0.4549451674
Kurtosis0.9929737534
Mean826.9835069
Median Absolute Deviation (MAD)0
Skewness-1.730005696
Sum60921394
Variance141550.6306
MonotonicityNot monotonic
2024-03-09T21:02:51.005869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
999 60930
82.7%
3 8032
 
10.9%
6 4470
 
6.1%
2 180
 
0.2%
22 44
 
0.1%
10 4
 
< 0.1%
4 4
 
< 0.1%
8 3
 
< 0.1%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
2 180
 
0.2%
3 8032
10.9%
4 4
 
< 0.1%
6 4470
6.1%
8 3
 
< 0.1%
ValueCountFrequency (%)
999 60930
82.7%
22 44
 
0.1%
10 4
 
< 0.1%
8 3
 
< 0.1%
6 4470
 
6.1%

CV_DURACION
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean827.9546337
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:51.255545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation376.0006761
Coefficient of variation (CV)0.4541319787
Kurtosis1.039352708
Mean827.9546337
Median Absolute Deviation (MAD)0
Skewness-1.743364015
Sum60992934
Variance141376.5084
MonotonicityNot monotonic
2024-03-09T21:02:51.457749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
999 61036
82.8%
1 8102
 
11.0%
2 4364
 
5.9%
5 109
 
0.1%
12 47
 
0.1%
3 5
 
< 0.1%
4 4
 
< 0.1%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
1 8102
11.0%
2 4364
5.9%
3 5
 
< 0.1%
4 4
 
< 0.1%
5 109
 
0.1%
ValueCountFrequency (%)
999 61036
82.8%
12 47
 
0.1%
5 109
 
0.1%
4 4
 
< 0.1%
3 5
 
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:51.655657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length13
Median length9
Mean length8.454803372
Min length4

Characters and Unicode

Total characters622840
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO APLICA
2nd rowNO APLICA
3rd rowNO APLICA
4th rowNO APLICA
5th rowNO APLICA
ValueCountFrequency (%)
no 61036
45.3%
aplica 61036
45.3%
años 8102
 
6.0%
semestres 4364
 
3.2%
cuatrimestres 109
 
0.1%
modular 47
 
< 0.1%
trimestres 5
 
< 0.1%
bimestres 4
 
< 0.1%
2024-03-09T21:02:52.145669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 130330
20.9%
O 69185
11.1%
I 61154
9.8%
C 61145
9.8%
L 61083
9.8%
N 61036
9.8%
61036
9.8%
P 61036
9.8%
S 21430
 
3.4%
E 13328
 
2.1%
Other values (7) 22077
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 561804
90.2%
Space Separator 61036
 
9.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 130330
23.2%
O 69185
12.3%
I 61154
10.9%
C 61145
10.9%
L 61083
10.9%
N 61036
10.9%
P 61036
10.9%
S 21430
 
3.8%
E 13328
 
2.4%
Ñ 8102
 
1.4%
Other values (6) 13975
 
2.5%
Space Separator
ValueCountFrequency (%)
61036
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 561804
90.2%
Common 61036
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 130330
23.2%
O 69185
12.3%
I 61154
10.9%
C 61145
10.9%
L 61083
10.9%
N 61036
10.9%
P 61036
10.9%
S 21430
 
3.8%
E 13328
 
2.4%
Ñ 8102
 
1.4%
Other values (6) 13975
 
2.5%
Common
ValueCountFrequency (%)
61036
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614738
98.7%
None 8102
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 130330
21.2%
O 69185
11.3%
I 61154
9.9%
C 61145
9.9%
L 61083
9.9%
N 61036
9.9%
61036
9.9%
P 61036
9.9%
S 21430
 
3.5%
E 13328
 
2.2%
Other values (6) 13975
 
2.3%
None
ValueCountFrequency (%)
Ñ 8102
100.0%

CV_ESTATUS
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.053470346
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:52.388826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.281522199
Coefficient of variation (CV)0.26723315
Kurtosis44.76458185
Mean1.053470346
Median Absolute Deviation (MAD)0
Skewness6.248032323
Sum77606
Variance0.07925474854
MonotonicityNot monotonic
2024-03-09T21:02:53.040113image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
1 70625
95.9%
2 2303
 
3.1%
3 581
 
0.8%
4 158
 
0.2%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
1 70625
95.9%
2 2303
 
3.1%
3 581
 
0.8%
4 158
 
0.2%
ValueCountFrequency (%)
4 158
 
0.2%
3 581
 
0.8%
2 2303
 
3.1%
1 70625
95.9%
Distinct4
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size575.7 KiB
2024-03-09T21:02:53.240185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length11
Median length6
Mean length6.110537961
Min length6

Characters and Unicode

Total characters450145
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActiva
2nd rowActiva
3rd rowLatencia
4th rowActiva
5th rowActiva
ValueCountFrequency (%)
activa 70625
95.9%
latencia 2303
 
3.1%
liquidacion 581
 
0.8%
suspendido 158
 
0.2%
2024-03-09T21:02:53.641513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 75812
16.8%
i 74829
16.6%
c 73509
16.3%
t 72928
16.2%
A 70625
15.7%
v 70625
15.7%
n 3042
 
0.7%
L 2884
 
0.6%
e 2461
 
0.5%
d 897
 
0.2%
Other values (6) 2533
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 376478
83.6%
Uppercase Letter 73667
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 75812
20.1%
i 74829
19.9%
c 73509
19.5%
t 72928
19.4%
v 70625
18.8%
n 3042
 
0.8%
e 2461
 
0.7%
d 897
 
0.2%
u 739
 
0.2%
o 739
 
0.2%
Other values (3) 897
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
A 70625
95.9%
L 2884
 
3.9%
S 158
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 450145
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 75812
16.8%
i 74829
16.6%
c 73509
16.3%
t 72928
16.2%
A 70625
15.7%
v 70625
15.7%
n 3042
 
0.7%
L 2884
 
0.6%
e 2461
 
0.5%
d 897
 
0.2%
Other values (6) 2533
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 450145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 75812
16.8%
i 74829
16.6%
c 73509
16.3%
t 72928
16.2%
A 70625
15.7%
v 70625
15.7%
n 3042
 
0.7%
L 2884
 
0.6%
e 2461
 
0.5%
d 897
 
0.2%
Other values (6) 2533
 
0.6%

CV_MOTIVO
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.8870321854
Minimum0
Maximum20
Zeros68760
Zeros (%)93.3%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:53.846482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.558547782
Coefficient of variation (CV)4.011745955
Kurtosis15.25325958
Mean0.8870321854
Median Absolute Deviation (MAD)0
Skewness4.071539953
Sum65345
Variance12.66326232
MonotonicityNot monotonic
2024-03-09T21:02:54.066160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 68760
93.3%
17 2587
 
3.5%
7 1337
 
1.8%
15 370
 
0.5%
5 257
 
0.3%
19 128
 
0.2%
6 116
 
0.2%
20 45
 
0.1%
18 36
 
< 0.1%
16 31
 
< 0.1%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
0 68760
93.3%
5 257
 
0.3%
6 116
 
0.2%
7 1337
 
1.8%
15 370
 
0.5%
ValueCountFrequency (%)
20 45
 
0.1%
19 128
 
0.2%
18 36
 
< 0.1%
17 2587
3.5%
16 31
 
< 0.1%

C_MOTIVO
Text

MISSING 

Distinct9
Distinct (%)0.2%
Missing68772
Missing (%)93.3%
Memory size575.7 KiB
2024-03-09T21:02:54.305738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length78
Median length16
Mean length20.02262075
Min length15

Characters and Unicode

Total characters98251
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCausa administrativa
2nd rowFalta de alumnos
3rd rowFalta de alumnos
4th rowFalta de alumnos
5th rowFalta de alumnos
ValueCountFrequency (%)
de 3015
20.4%
falta 2618
17.7%
alumnos 2587
17.5%
causa 1337
9.0%
administrativa 1337
9.0%
la 442
 
3.0%
escuela 406
 
2.7%
está 370
 
2.5%
clausurada 370
 
2.5%
incumplimiento 257
 
1.7%
Other values (20) 2072
14.0%
2024-03-09T21:02:54.772309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 17511
17.8%
9904
10.1%
s 7292
 
7.4%
l 7043
 
7.2%
t 6580
 
6.7%
e 6139
 
6.2%
u 5768
 
5.9%
n 5470
 
5.6%
d 5303
 
5.4%
i 5215
 
5.3%
Other values (17) 22026
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83440
84.9%
Space Separator 9904
 
10.1%
Uppercase Letter 4907
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 17511
21.0%
s 7292
8.7%
l 7043
8.4%
t 6580
 
7.9%
e 6139
 
7.4%
u 5768
 
6.9%
n 5470
 
6.6%
d 5303
 
6.4%
i 5215
 
6.2%
m 4647
 
5.6%
Other values (10) 12472
14.9%
Uppercase Letter
ValueCountFrequency (%)
F 2618
53.4%
C 1510
30.8%
L 370
 
7.5%
I 257
 
5.2%
E 116
 
2.4%
N 36
 
0.7%
Space Separator
ValueCountFrequency (%)
9904
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88347
89.9%
Common 9904
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 17511
19.8%
s 7292
8.3%
l 7043
8.0%
t 6580
 
7.4%
e 6139
 
6.9%
u 5768
 
6.5%
n 5470
 
6.2%
d 5303
 
6.0%
i 5215
 
5.9%
m 4647
 
5.3%
Other values (16) 17379
19.7%
Common
ValueCountFrequency (%)
9904
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97637
99.4%
None 614
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 17511
17.9%
9904
10.1%
s 7292
 
7.5%
l 7043
 
7.2%
t 6580
 
6.7%
e 6139
 
6.3%
u 5768
 
5.9%
n 5470
 
5.6%
d 5303
 
5.4%
i 5215
 
5.3%
Other values (15) 21412
21.9%
None
ValueCountFrequency (%)
á 370
60.3%
ó 244
39.7%

SUBSISTEMA_1
Text

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing18523
Missing (%)25.1%
Memory size575.7 KiB
2024-03-09T21:02:54.973750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length54
Median length40
Mean length17.11873595
Min length8

Characters and Unicode

Total characters944201
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentralizados SEMS
2nd rowCentralizados SEMS
3rd rowCentralizados SEMS
4th rowCentralizados SEMS
5th rowCentralizados SEMS
ValueCountFrequency (%)
estado 30677
33.7%
particular 19169
21.1%
descentralizados 18708
20.5%
centralizados 13206
14.5%
autónomo 2957
 
3.2%
sems 1091
 
1.2%
subsidiados 1068
 
1.2%
de 1068
 
1.2%
federativas 711
 
0.8%
entidades 711
 
0.8%
Other values (9) 1691
 
1.9%
2024-03-09T21:02:55.417603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 137190
14.5%
t 86199
 
9.1%
s 85694
 
9.1%
r 71041
 
7.5%
o 69681
 
7.4%
d 67982
 
7.2%
i 54647
 
5.8%
e 53949
 
5.7%
l 52157
 
5.5%
c 37979
 
4.0%
Other values (19) 227682
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 815028
86.3%
Uppercase Letter 93272
 
9.9%
Space Separator 35901
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 137190
16.8%
t 86199
10.6%
s 85694
10.5%
r 71041
8.7%
o 69681
8.5%
d 67982
8.3%
i 54647
 
6.7%
e 53949
 
6.6%
l 52157
 
6.4%
c 37979
 
4.7%
Other values (9) 98509
12.1%
Uppercase Letter
ValueCountFrequency (%)
E 33024
35.4%
P 19714
21.1%
D 18756
20.1%
C 13206
 
14.2%
S 3801
 
4.1%
A 2957
 
3.2%
M 1091
 
1.2%
F 717
 
0.8%
O 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
35901
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 908300
96.2%
Common 35901
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 137190
15.1%
t 86199
9.5%
s 85694
9.4%
r 71041
 
7.8%
o 69681
 
7.7%
d 67982
 
7.5%
i 54647
 
6.0%
e 53949
 
5.9%
l 52157
 
5.7%
c 37979
 
4.2%
Other values (18) 191781
21.1%
Common
ValueCountFrequency (%)
35901
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 941238
99.7%
None 2963
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 137190
14.6%
t 86199
 
9.2%
s 85694
 
9.1%
r 71041
 
7.5%
o 69681
 
7.4%
d 67982
 
7.2%
i 54647
 
5.8%
e 53949
 
5.7%
l 52157
 
5.5%
c 37979
 
4.0%
Other values (17) 224719
23.9%
None
ValueCountFrequency (%)
ó 2957
99.8%
í 6
 
0.2%

SUBSISTEMA_2
Text

MISSING 

Distinct26
Distinct (%)< 0.1%
Missing18523
Missing (%)25.1%
Memory size575.7 KiB
2024-03-09T21:02:55.646421image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length35
Median length33
Mean length15.229005
Min length3

Characters and Unicode

Total characters839971
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDGB
2nd rowDGB
3rd rowDGB
4th rowDGB
5th rowDGB
ValueCountFrequency (%)
particular 19169
25.0%
telebachillerato 16087
20.9%
comunitario 10002
13.0%
estatal 5747
 
7.5%
bachillerato 5657
 
7.4%
cobach 4767
 
6.2%
emsad 3754
 
4.9%
autónomas 2886
 
3.8%
estatales 2886
 
3.8%
universidades 2886
 
3.8%
Other values (26) 2983
 
3.9%
2024-03-09T21:02:56.114491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 126848
15.1%
L 87520
10.4%
T 87258
10.4%
E 77491
9.2%
R 74245
8.8%
I 66990
8.0%
C 62119
7.4%
O 50615
 
6.0%
U 35014
 
4.2%
B 28033
 
3.3%
Other values (13) 143838
17.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 818191
97.4%
Space Separator 21764
 
2.6%
Other Punctuation 16
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 126848
15.5%
L 87520
10.7%
T 87258
10.7%
E 77491
9.5%
R 74245
9.1%
I 66990
8.2%
C 62119
7.6%
O 50615
 
6.2%
U 35014
 
4.3%
B 28033
 
3.4%
Other values (11) 122058
14.9%
Space Separator
ValueCountFrequency (%)
21764
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 818191
97.4%
Common 21780
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 126848
15.5%
L 87520
10.7%
T 87258
10.7%
E 77491
9.5%
R 74245
9.1%
I 66990
8.2%
C 62119
7.6%
O 50615
 
6.2%
U 35014
 
4.3%
B 28033
 
3.4%
Other values (11) 122058
14.9%
Common
ValueCountFrequency (%)
21764
99.9%
, 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 837085
99.7%
None 2886
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 126848
15.2%
L 87520
10.5%
T 87258
10.4%
E 77491
9.3%
R 74245
8.9%
I 66990
8.0%
C 62119
7.4%
O 50615
 
6.0%
U 35014
 
4.2%
B 28033
 
3.3%
Other values (12) 140952
16.8%
None
ValueCountFrequency (%)
Ó 2886
100.0%

SUBSISTEMA_3
Text

MISSING 

Distinct33
Distinct (%)0.1%
Missing18523
Missing (%)25.1%
Memory size575.7 KiB
2024-03-09T21:02:56.347708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length95
Median length74
Mean length38.56592211
Min length18

Characters and Unicode

Total characters2127142
Distinct characters54
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCentro de Estudios de Bachillerato
2nd rowCentro de Estudios de Bachillerato
3rd rowCentro de Estudios de Bachillerato
4th rowCentro de Atención a Estudiantes con Discapacidad
5th rowCentro de Atención a Estudiantes con Discapacidad
ValueCountFrequency (%)
de 42040
16.9%
general 28491
11.4%
bachillerato 25160
10.1%
escuela 19223
 
7.7%
particular 19169
 
7.7%
telebachillerato 16087
 
6.5%
estatal 11563
 
4.6%
centro 10644
 
4.3%
comunitario 10146
 
4.1%
estado 5800
 
2.3%
Other values (73) 60798
24.4%
2024-03-09T21:02:56.859110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 258621
12.2%
e 252039
11.8%
l 208554
9.8%
194107
 
9.1%
r 159190
 
7.5%
i 133809
 
6.3%
t 130946
 
6.2%
c 108849
 
5.1%
o 108784
 
5.1%
d 70716
 
3.3%
Other values (44) 501527
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1735385
81.6%
Uppercase Letter 196308
 
9.2%
Space Separator 194240
 
9.1%
Connector Punctuation 774
 
< 0.1%
Other Punctuation 152
 
< 0.1%
Close Punctuation 140
 
< 0.1%
Open Punctuation 140
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 258621
14.9%
e 252039
14.5%
l 208554
12.0%
r 159190
9.2%
i 133809
7.7%
t 130946
7.5%
c 108849
6.3%
o 108784
6.3%
d 70716
 
4.1%
n 68033
 
3.9%
Other values (17) 235844
13.6%
Uppercase Letter
ValueCountFrequency (%)
E 44631
22.7%
B 33727
17.2%
G 28499
14.5%
C 27110
13.8%
P 20446
10.4%
T 16098
 
8.2%
S 7672
 
3.9%
D 5062
 
2.6%
M 4545
 
2.3%
A 4162
 
2.1%
Other values (9) 4356
 
2.2%
Space Separator
ValueCountFrequency (%)
194107
99.9%
  133
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 140
92.1%
, 12
 
7.9%
Connector Punctuation
ValueCountFrequency (%)
_ 774
100.0%
Close Punctuation
ValueCountFrequency (%)
) 140
100.0%
Open Punctuation
ValueCountFrequency (%)
( 140
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1931693
90.8%
Common 195449
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 258621
13.4%
e 252039
13.0%
l 208554
10.8%
r 159190
 
8.2%
i 133809
 
6.9%
t 130946
 
6.8%
c 108849
 
5.6%
o 108784
 
5.6%
d 70716
 
3.7%
n 68033
 
3.5%
Other values (36) 432152
22.4%
Common
ValueCountFrequency (%)
194107
99.3%
_ 774
 
0.4%
) 140
 
0.1%
. 140
 
0.1%
( 140
 
0.1%
  133
 
0.1%
, 12
 
< 0.1%
- 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2117961
99.6%
None 9181
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 258621
12.2%
e 252039
11.9%
l 208554
9.8%
194107
 
9.2%
r 159190
 
7.5%
i 133809
 
6.3%
t 130946
 
6.2%
c 108849
 
5.1%
o 108784
 
5.1%
d 70716
 
3.3%
Other values (38) 492346
23.2%
None
ValueCountFrequency (%)
ó 8796
95.8%
é 140
 
1.5%
  133
 
1.4%
í 100
 
1.1%
ü 6
 
0.1%
á 6
 
0.1%

PERIODO
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2020.506781
Minimum2019
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size575.7 KiB
2024-03-09T21:02:57.088730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2019
5-th percentile2019
Q12020
median2021
Q32022
95-th percentile2022
Maximum2022
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.116240826
Coefficient of variation (CV)0.0005524558672
Kurtosis-1.35584227
Mean2020.506781
Median Absolute Deviation (MAD)1
Skewness-0.007664355164
Sum148844673
Variance1.245993581
MonotonicityIncreasing
2024-03-09T21:02:57.272880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2021 18511
25.1%
2022 18503
25.1%
2020 18469
25.1%
2019 18184
24.7%
(Missing) 12
 
< 0.1%
ValueCountFrequency (%)
2019 18184
24.7%
2020 18469
25.1%
2021 18511
25.1%
2022 18503
25.1%
ValueCountFrequency (%)
2022 18503
25.1%
2021 18511
25.1%
2020 18469
25.1%
2019 18184
24.7%